Method for predicting activation energy using atomic fingerprint descriptor or atomic descriptor

ABSTRACT

The present invention provides a method for constructing a database of atomic fingerprint descriptors. The invention provides a method for predicting activation energy using an atomic fingerprint descriptor and an atomic descriptor, the method comprising the steps of: (i) calculating the atomic fingerprint descriptor of a substrate; (ii) comparing the calculated atomic fingerprint descriptor with the constructed atomic fingerprint descriptor database to select an atomic position where cytochrome P450-mediated metabolism occurs; and (iii) predicting activation energy for the selected atomic position using an atomic descriptor. Also, the invention provides a method of predicting the activation energy of CYP450-mediated phase I metabolism using effective atomic descriptors. Specifically, the invention provides a method of predicting the activation energy either for cytochrome P450-mediated hydrogen abstraction or for tetrahedral intermediate formation in cytochrome P450-aromatic hydroxylation using equations including effective atomic descriptors.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of Ser. No. 13/001,579, filed Jan. 6,2011, which is the U.S. national phase application, pursuant to 35U.S.C. §371, of PCT/KR2009/006660, filed Nov. 12, 2009, designating theUnited States, which claims priority to Korean Application No.10-2008-0112389, filed Nov. 12, 2008, and Korean Application No.10-2009-0108741, filed Nov. 11, 2009.

BACKGROUND OF THE DISCLOSURE

1. Technical Field

The present invention relates to a method for predicting the activationenergy of phase I metabolism, mediated by CYP450 enzymes, using aneffective atomic fingerprint descriptor or atomic descriptor.

2. Related Art

The prediction of absorption, distribution, metabolism and excretion(ADME) properties of drugs is a very important technique to shorten thedrug development period and to enhance the probability of success ofdrug development. Among the drug's ADME properties, drug metabolism is akey determinant of metabolic stability, drug-drug interactions, and drugtoxicity.

Metabolic reactions can be divided according to the reaction mechanisminto two categories: aliphatic hydroxylation and aromatic hydroxylation.Also, they can be divided according to the type of reaction into thefollowing categories: N-dealkylation, C-hydroxylation, N-oxidation,O-dealkylation and the like. In aliphatic hydroxylation, the iron (Fe)of compound I in the active site of CYP450 (cytochrome P450) issubstituted with the hydrogen of the substrate, so that the substratebecomes a radical. Then, a hydroxyl group binds to the substrate to forma metabolite. In aromatic hydroxylation, the iron of compound I binds tothe substrate to form a tetrahedral intermediate, and then becomesdetached from the substrate while giving a hydroxyl group to thesubstrate, thereby forming a metabolite.

The metabolism of the compound may occur at most positions to whichhydrogen is bound. The possibility of reaction at each position dependson how the compound binds well to CYP450 and how the reactivity at thebound position is high. To determine accessibility, a docking study onCYP450 can be carried out, followed by calculation of binding affinity.

Prediction of the metabolisms of external substances is important in theearly stage of new drug development. Particularly, the reaction rate andregioselectivity of phase I metabolism are very importantpharmacokinetic characteristics, through which the toxicity ofmetabolites can be predicted.

Such reaction rate and regioselectivity can be predicted from activationenergy, but existing methods depend on time-consuming quantum mechanicalcalculations and difficult experiments. For example, K. R. Korzekwa etal. (J. Am. Chem. Soc. 1990, 112, 7042) reported a method of predictingthe activation energy for hydrogen abstraction by quantum mechanicalcalculation, and T. S. Dowers et al. (Drug Metab. Dispos. 2004, 32, 328)reported a method of predicting the activation energy of aromatichydroxylation by quantum mechanical calculation. However, such quantummechanical methods perform calculations in various molecular states, andthus cannot determine accurate activation energy due to the complexityresulting from the conformational difference between these states.

Accordingly, the present inventors have developed a novel, fast andaccurate model which can predict the activation energy of phase Imetabolism on the basis of only the characteristics of an externalsubstrate using an atomic fingerprint descriptor or an atomicdescriptor, thereby completing the present invention.

SUMMARY OF THE DISCLOSURE

It is an object of the present invention to provide a method forconstructing a database of atomic fingerprint descriptors.

Another object of the present invention is to provide a method forpredicting activation energy using an atomic fingerprint descriptor andan atomic descriptor.

Still another object of the present invention is to provide a method forpredicting activation energy using an atomic descriptor.

Still another object of the present invention is to provide a method ofpredicting i) a metabolite, ii) the relative rate of metabolism, iii)the regioselectivity of metabolism, iv) the inhibition of metabolism, v)a drug-drug interaction, and vi) the toxicity of a metabolite, throughthe activation energy predicted by said methods.

To achieve the above objects, the present invention provides a methodfor constructing a database of atomic fingerprint descriptors, themethod comprising the steps of:

(i) calculating the atomic fingerprint descriptor of a substrate, whichis represented by the following equation 1;

(ii) predicting activation energy for an atomic position using an atomicdescriptor;

(iii) predicting cytochrome P450-mediated metabolism using the predictedactivation energy; and

(iv) comparing the predicted metabolism with experimental metabolism andstoring whether the metabolism occurs:Xabc  [Equation 1]wherein X is the chemical symbol of an atom; a is a bond indicator thatindicates the number of atoms bonded; b is a ring indicator thatindicates whether the atom is part of a ring; and c is an aromaticindicator that indicates whether the atom is an aromatic atom.

The metabolism in step (iii) is aliphatic hydroxylation or aromatichydroxylation.

Also, the metabolism in step (iii) is N-dealkylation, C-hydroxylation,N-oxidation or O-dealkylation.

The present invention can be applied to all CYP 450 enzymes, and it isapparent that the present invention can be applied particularly to humanCYP 450 enzymes. The cytochrome P450 enzymes according to the presentinvention include, but are not limited to, CYP2E1, CYP3A4, CYP2B6,CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1, and CYP2A6.

In another aspect, the present invention provides a method forpredicting activation energy using an atomic fingerprint descriptor andan atomic descriptor, the method comprising the steps of:

(i) calculating the atomic fingerprint descriptor of a substrate, whichis represented by the following equation 1;

(ii) comparing the calculated atomic fingerprint descriptor with thedata, constructed by said method, to select an atomic position wherecytochrome P450-mediated metabolism can occur; and

(iii) predicting activation energy for the selected atomic positionusing an atomic descriptor:Xabc  [Equation 1]wherein X is the chemical symbol of an atom; a is a bond indicator thatindicates the number of atoms bonded; b is a ring indicator thatindicates whether the atom is part of a ring; and c is an aromaticindicator that indicates whether the atom is an aromatic atom.

The metabolism in step (ii) is aliphatic hydroxylation or aromatichydroxylation.

Also, the metabolism in step (ii) is N-dealkylation, C-hydroxylation,N-oxidation or O-dealkylation.

Examples of the cytochrome P450 enzyme include, but are not limited to,CYP2E1, CYP3A4, CYP2B6, CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6,CYP1B1, and CYP2A6.

In step (iii), the activation energy for cytochrome P450-mediatedhydrogen abstraction from a substrate of the following formula 1 can bepredicted using the atomic descriptors [δ_(het)], [max(δ_(heavy))],[μ_(C—H)] and

$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack:$

wherein the circle together with Fe—O indicates an oxyferrylintermediate; [δ_(het)] indicates the net atomic charge of a heteroatomin the alpha-position relative to the reaction center; [max(δ_(heavy))]indicates the highest atomic charge in X¹, X² and X³ which are neitherhydrogen nor helium; [μ_(C—H)] indicates the bond dipole of thecarbon-hydrogen bond; and

$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack$indicates the sum of the atomic polarizabilities of H, C, X¹, X² and X³.

According to the present invention, the atomic descriptors [δ_(het)] and[max(δ_(heavy))] can be calculated, and activation energy can becalculated according to the following equation 1-1:E _(a) ^(Habs) ^(—) ^((B))=25.94+1.88*[δ_(het)]+1.03*[max(δ_(heavy))]wherein E_(a) ^(Habs) ^(—) ^((B)) indicates activation energy requiredfor abstraction of hydrogen attached to a carbon atom having aheteroatom in the alpha-position relative to the reaction center.

Also, according to the present invention, the atomic descriptors[μ_(C—H)] and

$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack$can be calculated, and activation energy can be calculated according tothe following equation 1-2:

$\begin{matrix}{E_{a}^{{Habs\_}{(A)}} = {28.50 - {2.22*\lbrack \mu_{C - H} \rbrack} + {1.12*\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack}}} & \lbrack {{Equation}\mspace{14mu} 1\text{-}2} \rbrack\end{matrix}$wherein E_(a) ^(Habs) ^(—) ^((A)) indicates activation energy requiredfor abstraction of hydrogen attached to a carbon atom having noheteroatom in the alpha-position relative to the reaction center.

In step (iii), the activation energy for tetrahedral intermediateformation in cytochrome P450-mediated aromatic hydroxylation for asubstrate of the following formula 2 can be predicted using the atomicdescriptors [δ_(H)] and [mean(α_(alpha))]:

wherein the circle together with Fe—O indicates an oxyferrylintermediate; [δ_(H)] indicates the net atomic charge of the hydrogen ofthe substrate; and [mean(α_(alpha))] indicates the mean value of thepolarizabilities of adjacent carbon atoms.

According to the present invention, the atomic descriptors [δ_(H)] and[mean(α_(alpha))] can be calculated, and activation energy can becalculated according to the following equations:E _(a) ^(aro) ^(—)^(o,p)=21.34−0.75*[δ_(H)]−1.24*[mean(α_(alpha)])  [Equation 2-1]E _(a) ^(aro) ^(m) =22.14−0.68*[δ_(H)]−0.83*[mean(α_(alpha))]  [Equation2-2]E _(a) ^(aro) ^(—)^(0,2,3)=21.02−1.49*[δ_(H)]−0.92*[mean(α_(alpha))]  [Equation 2-3]wherein E_(a) ^(aro) ^(—) ^(o,p) indicates the activation energy fortetrahedral intermediate formation in a benzene having one substituentin the ortho/para-position; E_(a) ^(aro) ^(—) ^(m) indicates theactivation energy for tetrahedral intermediate formation in a benzenehaving one substituent in the meta-position; and E_(a) ^(aro) ^(—)^(0,2,3) indicates the activation energy for tetrahedral intermediateformation in a benzene having 0, 3 or 3 substituents.

In another aspect, the present invention provides a method forpredicting a metabolite using the activated energy predicted by saidmethod. Herein, an atomic position having the lowest activation energycan be predicted as a position where metabolism occurs.

In still another aspect, the present invention provides a method ofpredicting a drug-drug interaction through the activation energypredicted by said method.

As used herein, the term “drug-drug interaction” refers to the effectsthat occur when two or more drugs are used at the same time. Sucheffects include changes in the kinetics of drug absorption by theintestinal tract, changes in the rate of detoxification and eliminationof the drug by the liver or other organs, new or enhanced side effectsand changes in the drug's activity. CYP2C9 which is a CYP isoform is oneof the major enzymes that are involved in the phase I metabolism ofdrugs. The inhibition of this enzyme can result in an undesirabledrug-drug interaction or drug toxicity [see Lin, J. H.; Lu, A. Y. H.,Inhibition and induction of cytochrome P450 and the clinicalimplications. Clin. Pharmacokinet. 1998, 35 (5), 361-390]. Namely, ifthe activation energy of a substrate is relatively high, metabolism canbe inhibited to result in the inhibition of CYP450 enzymes, thus causingan undesirable drug-drug interaction.

Also, metabolites, obtained by oxidation or reduction of substrates bycytochromes, can cause toxicities such as chemical carcinogenesis ormutagenesis, and for this reason, it is very important to predictmetabolites, including substrate specificity for cytochromes (VermeulenN P E, Donne-Op den Kelder G, Commandeur J N M. Molecular mechanisms oftoxicology and drug design, in Trend in Drug Research, Proc. 7^(th)Noordwijkerhout-Camerino Symp., Claassen, V., Ed., Elsevier SciencePublishers, Amsterdam, 1990, 253).

The present invention provides a method of predicting a metabolite of aCYP450 enzyme by predicting binding possibility using an atomic-typefingerprint descriptor, which includes the type of atom and thesurrounding bond order, and by predicting reactivity using an atomicdescriptor. The method of the present invention solves a time-consumingproblem in predicting accessibility using the three-dimensionalstructure of a CYP450 enzyme and does not require any quantum mechanicalcalculation or experiment.

The atomic fingerprint descriptor for predicting the possibility ofbinding of a cytochrome P450 enzyme to a substrate can be expressed asfollows:

The atomic fingerprint descriptor consists of: the element symbol of anatom; a bond order indicating the number of atoms bonded; a ringindicator that indicates whether the atom is part of a ring; and anaromatic indicator that indicates whether the atom is one included in anaromatic group. This expression method intuitively and simply expressesthe type of atom and the surrounding bonding environment. However, theatomic fingerprint descriptor has its own information, but does not havethe surrounding bonded atoms, and for this reason, the surroundingenvironment is reflected by writing the surrounding bonded atomicfingerprint descriptors therewith. The larger the connectivity, the morethe information of the surrounding environment is included. However, ifatomic fingerprint descriptors become excessively large, over-fittingcan occur. In the present invention, when the information of atomsconnected directly to the atomic fingerprint descriptor was used, themost efficient calculation results were shown.

If atomic fingerprint descriptors for all the atomic positions of asubstrate are the same as the atomic fingerprint descriptions of themetabolism of the substrate used in a training set, it is determined tobe “on”, and if not so, it is determined to be “off”. Then, since thepositions where metabolic reactions can occur were determined, theprediction of reactivity is performed by calculating activation energy,and the relative order of priority is determined.

Prediction of the reactivity of cytochrome P450 enzymes with thesubstrates was carried out using the calculation methods described inKorean Patent Application No. 10-2008-0112389 (entitled “Method forpredicting activation energy using effective atomic descriptors).

Finally, the prediction of metabolic reactions of cytochrome P450enzymes with the substrates is performed through the prediction ofbinding possibility and the prediction of reactivity, and the activationenergies of individual positions are calculated using reactivityprediction models. The activation energies are arranged in the order oflower to higher energy, and three positions having lower activationenergies are determined to be positions at which metabolic reactions canoccur. The analysis of the results is carried out by determining whetherthe two positions selected as described include an experimentally knownmetabolic position.

To achieve another object, the present invention provides a method ofpredicting the activation energy for CYP450-mediated hydrogenabstraction according to an equation including an effective atomicdescriptor. This method of the present invention is fast and accurateand does not require any quantum mechanical calculation or experiment.

Hydrogen abstraction by a cytochrome P450 enzyme may be shown in thefollowing reaction scheme 1:

wherein the circle together with Fe—O indicates an oxyferrylintermediate.

The present invention provides a method of predicting the activationenergy for cytochrome P450-mediated hydrogen abstraction from asubstrate of the following formula 1 using the atomic descriptors[δ_(het)], [max(δ_(heavy))], [μ_(C—H)] and

$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack:$

wherein the circle together with Fe—O indicates an oxyferrylintermediate; [δ_(het)] indicates the net atomic charge of a heteroatomin the alpha-position relative to the reaction center; [max(δ_(heavy))]indicates the highest atomic charge in X¹, X² and X³ which are neitherhydrogen nor helium; [μ_(C—H)] indicates the bond dipole of thecarbon-hydrogen bond; and

$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack$indicates the sum of the atomic polarizabilities of the atoms H, C, X¹,X² and X³.

The present invention can be applied to all CYP 450 enzymes, and it isapparent that the present invention can be applied particularly to humanCYP 450 enzymes. The cytochrome P450 enzymes according to the presentinvention include, but are not limited to, CYP2E1, CYP3A4, CYP2B6,CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1, and CYP2A6.

In the method of predicting the activation energy, any C—H bond to atarget molecule can be recognized as a position where metabolism canoccur in the target molecule. If the C atom of the C—H bond of thetarget molecule is aliphatic carbon, it can be determined to be aposition where hydrogen abstraction can occur.

In hydrogen abstraction by the CYP450 enzyme, the type of atom can bedetermined depending on whether a heteroatom is present or not in thealpha-position with respect to the reaction center (C—H where actualmetabolism occurs).

If there is a heteroatom in the alpha-position relative to the reactioncenter, the atomic descriptors [δ_(het)] and [max(δ_(heavy))] can becalculated, and the activation energy for hydrogen abstraction can bepredicted according to the following equation 1-1:E _(a) ^(Habs) ^(—)^((B))=25.94+1.88*[δ_(het)]+1.03*[max(δ_(heavy))]  [Equation 1-1]wherein E_(a) ^(Habs) ^(—) ^((B)) indicates activation energy requiredfor abstraction of hydrogen attached to a carbon atom having aheteroatom in the alpha-position relative to the reaction center.

If there is no heteroatom in the alpha-position relative to the reactioncenter, the atomic descriptors can be calculated, and the activationenergy for hydrogen abstraction can be predicted according to thefollowing equation [1-2]:

$\begin{matrix}{E_{a}^{{Habs\_}{(A)}} = {28.50 - {2.22*\lbrack \mu_{C - H} \rbrack} + {1.12*\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack}}} & \lbrack {{Equation}\mspace{14mu} 1\text{-}2} \rbrack\end{matrix}$wherein E_(a) ^(Habs) ^(—) ^((A)) indicates activation energy requiredfor abstraction of hydrogen attached to a carbon atom having noheteroatom in the alpha-position relative to the reaction center.

To achieve another object, the present invention provides a method ofpredicting the activation energy for CYP450-mediated aromatichydroxylation according to an equation including an effective atomicdescriptor. The method of the present invention is fast and accurate anddoes not require any quantum mechanical calculation or experiment.

The tetrahedral intermediate formation reaction in cytochromeP450-mediated aromatic hydroxylation may be shown in the followingreaction scheme 2:

wherein the circle together with Fe—O indicates an oxyferrylintermediate.

The present invention provides a method of predicting the activationenergy for tetrahedral intermediate formation in cytochromeP450-mediated aromatic hydroxylation for a substrate of the followingformula 2 using the atomic descriptors [δ_(H)] and [mean(α_(alpha))]:

wherein the circle together with Fe—O indicates an oxyferrylintermediate; [δ_(H)] indicates the net atomic charge of the hydrogen;and [mean(α_(alpha))] indicates the mean value of the polarizabilitiesof adjacent carbon atoms.

The present invention may be applied to all CYP 450 enzymes, and it isapparent that the present invention can be applied particularly to humanCYP 450 enzymes. The cytochrome P450 enzymes according to the presentinvention include, but are not limited to, CYP2E1, CYP3A4, CYP2B6,CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1, and CYP2A6.

In the method of predicting the activation energy for tetrahedralintermediate formation, any C—H bond to a target molecule can bedetermined to be a position where metabolism can occur in the targetmolecule. Also, if the C atom of the C—H bond of the target molecule isaromatic carbon, it can be determined to be a metabolic position wherearomatic hydroxylation can occur.

According to the present invention, the atomic descriptors [(δ_(H)] and[mean(α_(alpha))] can be calculated, and the activation energy can bepredicted according to the following equations:E _(a) ^(aro) ^(—)^(o,p)=21.34−0.75*[δ_(H)]−1.24*[mean(α_(alpha))]  [Equation 2-1]E _(a) ^(aro) ^(m) =22.14−0.68*[δ_(H)]−0.83*[mean(α_(alpha))]  [Equation2-2]E _(a) ^(aro) ^(—)^(0,2,3)=21.02−1.49*[δ_(H)]−0.92*[mean(α_(alpha))]  [Equation 2-3]wherein E_(a) ^(aro) ^(—) ^(o,p) indicates the activation energy fortetrahedral intermediate formation in a benzene having one substituentin the ortho/para-position; E_(a) ^(aro) ^(—) ^(m) indicates theactivation energy for tetrahedral intermediate formation in a benzenehaving one substituent in the meta-position; and E_(a) ^(aro) ^(—) ^(m)indicates the activation energy for tetrahedral intermediate formationin a benzene having 0, 2 or 3 substituents.

In another aspect, the present invention provides a method of predictingthe relative rate of metabolism (k) according to the following Arrheniusequation 2 using the activation energy predicted by said method:k=Ae ^(−E) ^(a) ^(/RT)  [Equation 2]wherein k is a reaction rate constant, A is a frequency factor, E_(a) isactivation energy, R is a gas constant, and T is absolute temperature.

The reason why the above equation 2 was designed is because of theatomic fraction f=e^(−Ea/RT) exceeding activation energy. Namely,because only a molecule exceeding activation energy can cause areaction, the reaction rate constant is determined by the ratioexceeding activation energy.

In another aspect, the present invention provides a method of predictingmetabolic regioselectivity using the activation energy predicted by saidmethod.

More specifically, the present invention provides a method of predictingthe relative rate of metabolism according to the Arrhenius equationusing the activation energy predicted by said method and predictingmetabolic regioselectivity according to the following reaction scheme 3and equation 3 using the predicted relative rate of metabolism:

$\begin{matrix}{\frac{P_{1}}{P_{2}} = {\frac{\lbrack {ES}_{1} \rbrack}{\lbrack {ES}_{2} \rbrack}\frac{k_{5}}{k_{6}}}} & \lbrack {{Equation}\mspace{14mu} 3} \rbrack\end{matrix}$wherein P indicates the relative probability of formation of anymetabolite of all possible metabolites of a substrate, E is an enzyme, Sis a substrate, ES is an enzyme-substrate complex, [ES] is theconcentration of the enzyme-substrate complex, and k is a reaction rateconstant.

Namely, once the reaction rate of each atom in one molecule isdetermined according to the Arrhenius equation, the regioselectivity inthe molecule can be determined, because metabolism occurs as thereaction rate decreases. [see Higgins, L.; Korzekwa, K. R.; Rao, S.;Shou, M.; Jones, J. P., An assessment of the reaction energetics forcytochrome P450-mediated reactions. Arch. Biochem. Biophys. 2001, 385,220-230].

In still another aspect, the present invention provides a method ofpredicting the inhibition of metabolism using the activation energypredicted by said method. For example, it can be predicted that, if asubstrate has relatively high activation energy, the substrate will notbe metabolized, and thus will remain in the active site of CYP450enzymes.

In still another aspect, the present invention provides a method ofpredicting a drug-drug interaction using the activation energy predictedby said method.

As used herein, the term “atomic fingerprint descriptor” refers to avalue defined to express the type of atom and the surrounding bondingenvironment. It consists of the element symbol of an atom, a bond orderindicating the number of atoms bonded, a ring indicator that indicateswhether the atom is part of a ring, and an aromatic indicator thatindicates whether the atom is one included in an aromatic group.

As used herein, the term “atomic descriptor” refers to a value definedto reflect the properties of an atom itself and the bonding environmentof the atom. Examples of atomic descriptors that are used in the presentinvention include, but are not limited to, [δ_(het)], [max(δ_(heavy))],[μ_(C—H)],

$\begin{matrix}{\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack,} & \;\end{matrix}$[δ_(H)], [mean(α_(alpha))], etc.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentinvention will be more clearly understood from the following detaileddescription taken in conjunction with the accompanying drawing, inwhich:

FIG. 1 is a flowchart showing a method of constructing a database ofatomic fingerprint descriptors according to the present invention;

FIG. 2 is a flowchart showing a method of predicting activation energyusing an atomic fingerprint descriptor and an atomic descriptoraccording to the present invention and predicting i) a metabolite, ii)the relative rate of metabolism, iii) the regioselectivity ofmetabolism, iv) the inhibition of metabolism, v) a drug-druginteraction, and vi) the toxicity of a metabolite;

FIG. 3 is a flowchart showing a method of predicting activation energyusing atomic descriptors according to the present invention;

FIG. 4 shows the correlation between the quantum-mechanically calculatedactivation energy (QM E_(a)) for CYP450-mediated hydrogen abstractionand the activation energy (Predicted E_(a)) predicted according to thepresent invention; and

FIG. 5 shows the correlation between the quantum-mechanically calculatedactivation energy (QM E_(a)) for CYP450-mediated aromatic hydroxylationand the activation energy (Predicted E_(a)) predicted according to thepresent invention.

DETAILED DESCRIPTION OF THE DISCLOSURE

Hereinafter, the elements and technical features of the presentinvention will be described in further detail with reference toexamples. It is to be understood, however, that these examples are forillustrative purposes only and are not to be construed to limit thescope of the present invention. All literature cited herein isincorporated by reference.

EXAMPLES Example 1 Construction of Database of Atomic FingerprintDescriptors

As shown in FIG. 1, the present inventors constructed a database ofatomic fingerprint descriptors through a training method comprising thefollowing steps (see FIG. 1):

(i) calculating the atomic fingerprint descriptor of a substrate, whichis represented by the following equation 1;

(ii) predicting activation energy for an atomic position using an atomicdescriptor;

(iii) predicting cytochrome P450-mediated metabolism using the predictedactivation energy; and

(iv) comparing the predicted metabolism with experimental metabolism andstoring whether the predicted metabolism occurs:Xabc  [Equation 1]wherein X is the chemical symbol of an atom; a is a bond order thatindicates the number of atoms bonded; b is a ring indicator thatindicates whether the atom is part of a ring; and c is an aromaticindicator that indicates whether the atom is an aromatic atom.

Using the above-constructed database of atomic fingerprint descriptors,the possibility of reaction of the atomic fingerprint descriptor of agiven substrate with each CYP450 isoform was analyzed.

TABLE 1 Results of analysis for possibility of reaction of a givensubstrate using constructed atomic fingerprint descriptor database NO.Atomic fingerprint descriptors CYP1A2 CYP2C9 CYP2D6 CYP3A4 1C400C400H100H100H100 1 1 −1 1 2 C400C361C400H100H100 1 1 −1 1 3C361C361C361H100 1 1 1 1 4 C400C361H100H100H100 1 1 1 1 5C460C460H100H100N360 −1 1 1 1 6 C361C361H100N261 −1 −1 −1 1 7C460C460C460C460H100 −1 −1 −1 −1 8 C460C361C460C460H100 −1 −1 −1 −1 9C400C460H100H100H100 −1 −1 −1 −1 10 C400C400C400H100H100 1 1 1 1 11C460C360C460H100H100 1 1 1 1 12 C360C360C460H100 1 0 −1 1 13C300C360H100O100 0 0 0 1 14 C400C400C400H100N300 1 1 1 1 15C400H100H100H100O200 1 1 1 1 16 C400C400H100H100N300 1 1 1 1 17C400C351C400H100H100 −1 1 −1 −1 18 C361C351C361H100 1 −1 1 1 19C351C351H100N351 −1 −1 −1 −1 20 C400H100H100H100N300 1 1 1 1 21C460C400C460C460H100 0 −1 −1 −1 22 C460C460C460H100H100 −1 −1 1 1 23C460C360C460C460H100 0 −1 0 −1

In Table 1 above, “1” indicates that, in a training set, there is a casein which a reaction occurred in a site having the relevant atomicfingerprint descriptor. “−1” indicates that, in a training set, there isno case in which a reaction occurred in a site having the relevantatomic fingerprint descriptor. “0” indicates that an atom having therelevant atomic fingerprint descriptor does not exist in a training set.

Example 2 Prediction of Metabolite of 2-methoxyamphetamine Using thePrediction Method of the Present Invention

As shown in FIG. 2, the present inventors predicted activation energyusing a method comprising the following steps (see FIG. 2):

(i) calculating the atomic fingerprint descriptor of a substrate, whichis represented by the following formula 1;

(ii) comparing the calculated atomic fingerprint descriptor with thedata, constructed by the method of Example 1, to select an atomicposition where cytochrome P450-mediated metabolism can occur; and

(iii) predicting activation energy for the selected atomic positionusing an atomic descriptor:Xabc  [Equation 1]wherein X is the chemical symbol of an atom; a is a bond order thatindicates the number of atoms bonded; b is a ring indicator thatindicates whether the atom is part of a ring; and c is an aromaticindicator that indicates whether the atom is an aromatic atom.

After predicting the activation energy of 2-methoxyamphetamine using theabove method, the metabolite of 2-methoxyamphetamine was predicted.2-methoxyamphetamine has a chemical structure of the following formula3:

First, the positions of carbon atoms having hydrogen at positions 1, 2,3, 6, 7, 8, 9 and 10 were examined.

Then, the atomic fingerprint descriptors of positions 1, 2, 3, 6, 7, 8,9 and 10 were calculated and compared with the atomic fingerprintdescriptor database constructed in Example 10, thereby selecting anatomic position where metabolism may occur (see Table 1).

TABLE 2 Selection of atomic positions having the possibility ofmetabolism through the comparison of atomic fingerprint descriptorsAtomic Results of Possibility of position Atomic fingerprint descriptorcomparison metabolism Atom 1 C400C400H100H100H100 −1 Impossible Atom 2C400C400C400H100N300 1 Possible Atom 3 C400C361C400H100H100 −1Impossible Atom 6 C361C361C361H100 1 Possible Atom 7 C361C361C361H100 1Possible Atom 8 C361C361C361H100 1 Possible Atom 9 C361C361C361H100 1Possible Atom 10 C400H100H100H100O200 1 Possible

Then, the activation energies of the atomic positions having thepossibility of metabolism were calculated.

TABLE 3 Calculation of activation energies of atomic positions havingthe possibility of metabolism (see Example 6) Atomic position Activationenergy Atom 2 22.93 Atom 6 25.60 Atom 7 27.42 Atom 8 27.25 Atom 9 27.30Atom 10 22.22

Then, atomic position 10 having the lowest activation energy waspredicted as a position where a reaction occurs. Also, the followingmetabolite (formula 4) where O-dealkylation occurred at position 10 waspredicted in the following manner.

Example 3 Prediction of Metabolite Using Only Reactivity PredictionModel

A metabolite was predicted only with a reactivity prediction modelwithout considering the binding possibility of a substrate. Whenanalysis was carried out using a method of selecting two positionshaving the highest possibility, a predictability of about 62-70% wasgenerally shown.

TABLE 4 Results of metabolite prediction carried out using onlyreactivity prediction model N^(a) Nc^(b) Nc/N(%) CYP1A2 144 101 70.1CYP2C9 119 83 69.7 CYP2D6 146 91 62.3 CYP3A4 196 128 65.3 ^(a)Number ofsubstrates used in training; ^(b)Number of substrates that accuratelyreproduced an experimentally known metabolism.

Example 4 Prediction of Metabolite Using Accessibility Prediction Modeland Reactivity Prediction Model

In order to add the possibility of binding of various CYP450 enzymes tosubstrates, atomic fingerprint descriptors were used. A total of 185atomic fingerprint descriptors were used, and the possibility ofmetabolism by each CYP450 isoform was analyzed. Using a combination ofan accessibility prediction model and a reactivity prediction model, twopositions having the highest possibility and experimentally knownmetabolic positions were comparatively analyzed, and the results of theanalysis are shown in Table 5 below.

TABLE 5 N^(a) Nc^(b) Nc/N(%) CYP1A2 144 112 77.8 CYP2C9 119 92 77.3CYP2D6 146 102 69.9 CYP3A4 196 145 74.0 ^(a)Number of substrates used intraining; ^(b)Number of substrates that accurately reproduced anexperimentally known metabolism.

Generally, a predictability of 70-78% was shown, and the predictabilitywas more than 5% higher than that of Example 3 in which only thereactivity prediction model was used.

For reference, the substrates used in the metabolite prediction trainingaccording to each cytochrome P450 isoform in Tables 4 and 5 above areshown in the following Tables.

TABLE 6 Substrates used in training for prediction of metabolites withCYP1A2 (144 cases) Substrate 1 1-ethylpyrene 2 1-methylpyrene 32,3,7-trichlorooxanthrene 4(5S)-5-(3-hydroxyphenyl)-5-phenylimidazolidine-2,4-dione 5(5S)-5-(4-hydroxyphenyl)-5-phenylimidazolidine-2,4-dione 67-ethoxy-4-(trifluoromethyl)-2H-chromen-2-one 7 7-ethoxycoumarin 87-ethoxyresorufin 9 7-methoxyresorufin 101-[(2S)-4-(5-benzylthiophen-2-yl)but-3-yn-2-yl]urea 11 aflatoxin-b1 12all-trans-retinol 13 almotriptan 14 Ametryne 15 amitriptyline 16amodiaquine 17 Antipyrine 18 Apigenin 19 atomoxetine 20 Atrazine 21Azelastine 22 7-(benzyloxy)-4-(trifluoromethyl)-2H-chromen-2-one 23Biochainin-a 24 bropirimine 25 Bufuralol 26 Bunitrolol 27 bupivacaine 28Capsaicin 29 carbamazepine 30 Carbaryl 31 Carbofuran 32 Carvedilol 337-ethoxy-2-oxo-2H-chromene-3-carbonitrile 34 Celecoxib 35 chloroquine 36chlorpromazine 37 chlorpropamide 38 Cilostazol 39 Cisapride 40clomipramine 41 clozapine 422-chloro-3-(pyridin-3-yl)-5,6,7,8-tetrahydroindolizine-1- carboxamide 43curcumin 44 cyclobenzaprine 45 dacarbazine 46 dimethyl7,7′-dimethoxy-4,4′-bi-1,3-benzodioxole-5,5′- dicarboxylate 47 deprenyl48 dextromethorphan 49 dibenzo-a-h-anthracene 50 diclofenac 51dihydrodiol 52 dimethoxyisoflavone 53 dimethyloxoxanthene 54 dimmamc 55domperidone 56 doxepin 57 eletriptan 58 ellipticine 59estradiol-methyl-ether 60 estrone 61 etoricoxib 62 fenproporex 63fluoxetine 64 flurbiprofen 65 formononetin 66N-[2-(5-methoxy-1H-indol-3-yl)ethyl]-N-(propan-2- yl)propan-2-amine 67galangin 68 genistein 69 2-[(R)-{[5-(cyclopropylmethoxy)pyridin-3-yl]methyl}sulfinyl]-5-fluoro-1H-benzimidazole 70 harmaline 71 harmine 72hesperetin 73 imipramine 74 kaempferide 75 kaempferol 76N-carbamimidoyl-4-cyano-1-benzothiophene-2-carboxamide 77levobupivacaine 78 lidocaine 79 loratadine 804-(aminomethyl)-7-methoxy-2H-chromen-2-one 81 maprotiline 82(2R)-1-(1,3-benzodioxol-5-yl)-N-ethylpropan-2-amine 83(2R)-1-(1,3-benzodioxol-5-yl)-N-methylpropan-2-amine 843,8-dimethyl-3H-imidazo[4,5-f]quinoxalin-2-amine 85 melatonin 86mephenytoin 87 methoxychlor 88 methoxychlor-mono-oh 89 methyleugenol 90metoclopramide 91 mexiletine 92 mianserin 93 mirtazapine 94(2S)-1-(4-methylphenyl)-2-(pyrrolidin-1-yl)propan-1-one 951-methyl-4-phenyl-1,2,3,6-tetrahydropyridine 96 n-nitrosodiamylamine 97naproxen 98 naringenin 99 nefiracetam 100 nn-dimethyl-m-toluamide 1014-[methyl(nitroso)amino]-1-(pyridin-3-yl)butan-1-one 102 nordiazepam 103nortriptyline 104 ochratoxin-a 105 olanzapine 106 olopatadine 107(3S)-3-[3-(methylsulfonyl)phenyl]-1-propylpiperidine 108 oxycodone 109perazine 110 perphenazine 111 phenytoin 1121-methyl-6-phenyl-1H-imidazo[4,5-b]pyridin-2-amine 113 pimobendan 114progesterone 115 propafenone 116 propanolol 117 prunetin 118pyrazoloacridine 119 quinacrine 120 ropinirole 121 ropivacaine 122rosiglitazone 123 safrole 124 sertraline 125 sildenafil 126 stilbene 127(3Z)-3-[(3,5-dimethyl-1H-pyrrol-2-yl)methylidene]-1,3-dihydro-2H-indol-2-one 128 tacrine 129 tamarixetin 130 tangeretin 131tauromustine 132 terbinafine 133 terbuthylazine 134 testosterone 135theobromine 136 theophylline 137 tolperisone 138N-(2,6-dichlorobenzoyl)-4-(2,6-dimethoxy-phenyl)-L- phenylalanine 139trans-retinoic-acid 140 warfarin 141 zileuton 142 zolmitriptan 143zolpidem 144 zotepine

TABLE 7 Substrates used in training for prediction of metabolites withCYP2C9 (119 cases) Substrate 1 2n-propylquinoline 2(5S)-5-(3-hydroxyphenyl)- 5-phenylimidazolidine- 2,4-dione 3(5S)-5-(4-hydroxyphenyl)- 5-phenylimidazolidine- 2,4-dione 45-hydroxytryptamine 5 2-(trans-4-tert- butylcyclohexyl)-3-hydroxynaphthalene-1,4- dione 6 7-ethoxy-4- (trifluoromethyl)-2H-chromen-2-one 7 7-ethoxycoumarin 8 7-ethoxyresorufin 99-cis-retinoic-acid 10 1-[(2S)-4-(5- benzylthiophen-2-yl)but-3-yn-2-yl]urea 11 aceclofenac 12 Ametryne 13 amitriptyline 14 Antipyrine15 atomoxetine 16 7-(benzyloxy)-4- (trifluoromethyl)-2H- chromen-2-one17 Biochainin-a 18 Bufuralol 19 Capsaicin 20 carbamazepine 21 Carvedilol22 7-ethoxy-2-oxo-2H- chromene-3-carbonitrile 23 Celecoxib 24chlorpropamide 25 Cisapride 26 clomipramine 27 Clozapine 282-chloro-3-(pyridin-3-yl)- 5,6,7,8- tetrahydroindolizine-1- carboxamide29 N,4-dimethyl-N-(1-phenyl- 1H-pyrazol-5- yl)benzenesulfonamide 302-[(3S,4R)-3-benzyl-4- hydroxy-3,4-dihydro-2H- chromen-7-yl]-4-(trifluoromethyl)benzoic acid 31 cyclophosphamide 32 dimethyl7,7′-dimethoxy- 4,4′-bi-1,3-benzodioxole- 5,5′-dicarboxylate 33 Deprenyl34 dexloxiglumide 35 dextromethorphan 36 Diazepam 37dibenzo-a-h-anthracene 38 Diclofenac 39 Diltiazem 40 disopyramide 41doxepin 42 eletriptan 43 ellipticine 44 estradiol 45estradiol-methyl-ether 46 estrone 47 etodolac 48 etoperidone 49Fluoxetine 50 flurbiprofen 51 fluvastatin 52 N-[2-(5-methoxy-1H-indol-3-yl)ethyl]-N-(propan-2- yl)propan-2-amine 53 galangin 54 2-[(R)-{[5-(cyclopropylmethoxy)pyridin- 3-yl]methyl}sulfinyl]-5-fluoro-1H-benzimidazole 55 harmaline 56 harmine 57 hydromorphone 58ibuprofen 59 ifosfamide 60 imipramine 61 indomethacin 62 kaempferide 63ketamine 64 (1S,4S)-(6-dimethylamino- 4,4-diphenyl-heptan-3- yl)acetate65 lansoprazole 66 lidocaine 67 loratadine 68 lomoxicam 69 losartan 70luciferin 71 [(4E)-7-chloro-4- [(sulfooxy)imino]-3,4-dihydroquinolin-1(2H)- yl](2- methylphenyl)methanone 72 mefenamic-acid73 melatonin 74 meloxicam 75 mephenytoin 76 methadone 77methoxychlor-mono-oh 78 methyleugenol 79 mianserin 80 midazolam 81mirtazapine 82 4-{[(5S)-2,4-dioxo-1,3-thiazolidin-5-yl]methyl}-2-methox-N-[4- (trifluoromethyl)benzyl]benzamide 83(2S)-1-(4-methylphenyl)-2- (pyrrolidin-1-yl)propan-1-one 84n-nitrosodiamylamine 85 naproxen 86 nevirapine 87 ochratoxin-a 88omeprazole 89 oxybutynin 90 oxycodone 91 perazine 92 perphenazine 93phenacetin 94 phencyclidine 95 phenprocoumon 96 phenytoin 97 piroxicam98 progesterone 99 rosiglitazone 100 (5Z)-7-[(1S,2R,3R,4R)-3-benzenesulfonamidobicyclo[2.2.1] heptan-2-yl]hept-5-enoic acid 101sertraline 102 sildenafil 103 7-chloro-N-({5-[(dimethylamino)methyl]cyclopenta- 1,4-dien-1-yl}methyl)quinolin-4-amine 104 tamarixetin 105 tauromustine 106 temazepam 107 terbinafine 108testosterone 109 theophylline 110 tolbutamide 111 torasemide 112N-(2,6-dichlorobenzoyl)-4-(2,6- dimethoxy-phenyl)-L- phenylalanine 113trans-retinoic-acid 114 valdecoxib 115 valsartan 116 venlafaxine 117vivid-red 118 warfarin 119 zolpidem

TABLE 8 Substrates used in training for prediction of metabolites withCYP2D6 (146 cases) Substrate 1 2-(piperazin-1-yl)pyrimidine 22-methoxyamphetamine 3 4-methoxyamphetamine 42-(5-methoxy-1H-indol-3-yl)-N,N-dimethylethanamine 5 5-methoxytryptamine6 5-methoxytryptamine 7 7-ethoxycoumarin 8 all-trans-retinol 9all-trans-retinol 10 amitriptyline 11 amodiaquine 12 aripiprazole 13atomoxetine 14 atrazine 15 azelastine 16 biochainin-a 17 bisoprolol 18N-({4-[(5-bromopyrimidin-2-yl)oxy]-3-methylphenyl}carbamoyl)-2-(dimethylamino)benzamide 19 brofaromine 20bunitrolol 21 bupivacaine 22 capsaicin 23 carbamazepcapsaicinine 24carbamazepcapsaicinine 25 carbofuran 26 Carvedilol 277-ethoxy-2-oxo-2H-chromene-3-carbonitrile 28 celecoxib 29 celecoxib 30chlorpromazine 31 chlorpropamide 32 cibenzoline 33 cilostazol 34cisapride 35 citalopram 36 clomipramine 37 clozapine 38 codeine 39curcumin 40 cyclophosphamide 41 delavirdine 42 deprenyl 43dextromethorphan 44 diclofenac 45 dihydrocodeine 46 diltiazem 47 dimmamc48 domperidone 49 doxepin 50 2-(hydroxymethyl)-4-[5-(4-methoxyphenyl)-3-(trifluoromethyl)-1H-pyrazol-1-yl]benzenesulfonamide 51 eletriptan 52ellipticine 53 estradiol 54 estrone 55 etoperidone 56 etoricoxib 57eugenol 58 fenproporex 59 fluoxetine 60 fluvastatin 61N-[2-(5-methoxy-1H-indol-3-yl)ethyl]-N-(propan-2- yl)propan-2-amine 62galantamine 63 gefitinib 64 genistein 65 granisetron 66 harmaline 67harmine 68 hydrocodone 69 hydromorphone 70 ibogaine 71 iloperidone 72imipramine 73 cilostazol 74(1S,4S)-(6-dimethylamino-4,4-diphenyl-heptan-3-yl)acetate 75 lidocaine76 loratadine 77 4-(aminomethyl)-7-methoxy-2H-chromen-2-one 78maprotiline 79 (2R)-1-(1,3-benzodioxol-5-yl)-N-ethylpropan-2-amine 80(2R)-1-(1,3-benzodioxol-5-yl)-N-methylpropan-2-amine 81 melatonin 82mequitazine 83 meta-chlorophenylpiperazine 84 methadone 85 methadone 86methoxychlor-mono-oh 87 methoxyphenamine 88 methyleugenol 89metoclopramide 90 metoprolol 91 mexiletine 92 mianserin 93 minaprine 94mirtazapine 95 (2S)-1-(4-methoxyphenyl)-2-(pyrrolidin-1-yl)propan-1-one96 (2S)-1-(4-methylphenyl)-2-(pyrrolidin-1-yl)propan-1-one 971-methyl-4-phenyl-1,2,3,6-tetrahydropyridine 98 n-nitrosodiamylamine 99nevirapine 100 4-[methyl(nitroso)amino]-1-(pyridin-3-yl)butan-1-one 101nortriptyline 1025-(diethylamino)-2-methylpent-3-yn-2-yl(2S)-2-cyclohexyl-2-hydroxy-2-phenylacetate 103 olanzapine 104 omeprazole 105 ondansetron106 (3S)-3-[3-(methylsulfonyl)phenyl]-1-propylpiperidine 107 oxybutynin108 oxycodone 109 perazine 110 perphenazine 111 phenacetin 112phencyclidine 113 phenformin 114 phenytoin 115 pinoline 116(2R)-1-(4-methoxyphenyl)-N-methylpropan-2-amine 117 procainamide 118progesterone 119 promethazine 120 propafenone 121 propanolol 1223-(2-chlorophenyl)-N-[(1S)-1-(3- methoxyphenyl)ethyl]propan-1-amine 123reduced-dolasetron 124 ropivacaine 125 sertraline 126 sildenafil 127sparteine 128 spirosulfonamide 1297-chloro-N-({5-[(dimethylamino)methyl]cyclopenta-1,4-dien-1-yl}methyl)quinolin-4-amine 130 stilbene 131 stilbene 132tangeretin 133 tauromustine 134 tegaserod 135 testosterone 136theophylline 137 tolperisone 138 tramadol 139 traxoprodil 140tropisetron 141 valdecoxib 142 venlafaxine 143 warfarin 144 yohimbine145 zolpidem 146 zotepine

TABLE 9 Substrates used in training for prediction of metabolites withCYP3A4 (196 cases) Substrate 1 1-ethylpyrene 2 1-methylpyrene 32n-propylquinoline 4(5S)-5-(3-hydroxyphenyl)-5-phenylimidazolidine-2,4-dione 5(5S)-5-(4-hydroxyphenyl)-5-phenylimidazolidine-2,4-dione 65-methylchrysene 7 1-ethoxycoumarin 8 7-methoxyresorufin 91-[(2S)-4-(5-benzylthiophen-2-yl)but-3-yn-2-yl]urea 101-[(2S)-4-(5-benzylthiophen-2-yl)but-3-yn-2-yl]urea 11 acetochlor 12adinazolam 13 aflatoxin-b1 14 alachlor 15 alfentanil 16all-trans-retinol 17 almotriptan 18 dextromethorphan 19 ambroxol 20ametryne 21 amitriptyline 22 amodiaquine 23 androstenedione 24 apigenin25 aripiprazole 26 atomoxetine 27 atrazine 28 azelastine 297-(benzyloxy)-4-(trifluoromethyl)-2H-chromen-2-one 30 bisoprolol 31N-({4-[(5-bromopyrimidin-2-yl)oxy]-3-methylphenyl}carbamoyl)-2-(dimethylamino)benzamide 32 brotizolam 33budesonide 34 bufuralol 35 bupivacaine 36 bupropion 37 capsaicin 38carbamazepine 39 carbaryl 40 carbofuran 41 carvedilol 42 celecoxib 43cerivastatin 44 chloroquine 45 chlorpropamide 46 cibenzoline 47cisapride 48 citalopram 49 clobazam 50 clomipramine 51 clozapine 522-chloro-3-(pyridin-3-yl)-5,6,7,8-tetrahydroindolizine- 1-carboxamide 53cocaine 54 codeine 55 colchicine 56(3S)-3-(6-methoxypyridin-3-yl)-3-{2-oxo-3-[3-(5,6,7,8-tetrahydro-1,8-naphthyridin-2-yl)propyl]imidazolidin- 1-yl}propanoicacid 57 2-[(3S,4R)-3-benzyl-4-hydroxy-3,4-dihydro-2H-chromen-7-yl]-4-(trifluoromethyl)benzoic acid 58diethyl({[(2R,4S,7S)-1]-ethyl-6-methyl-6,11-diazatetracyclo[7.6.1.0{circumflex over ( )}{2,7}.0{circumflex over( )}{12,16}]hexadeca- 1(15),9,12(16),13-tetraen-4-yl]sulfamoyl})amine 59cyclobenzaprine 60 cyclophosphamide 61 dimethyl7,7′-dimethoxy-4,4′-bi-1,3-benzodioxole-5,5′- dicarboxylate 62delavirdine 63 deoxycholic-acid 64 deprenyl 65 deramciclane 66dexamethasone 67 dexloxiglumide 68 dextromethorphan 69dextropropoxyphene 70 (3S,8R,9S,10R,13S,14S)-3-hydroxy-10,13-dimethyl-1,2,3,4,7,8,9,11,12,14,15,16- dodecahydrocyclopenta[a]phenanthren-17-one71 diazepam 72 dibenzo-a-h-anthracene 73 diclofenac 74 dihydrocodeine 75dihydrodiol 76 diltiazem 77 disopyramide 78 domperidone 79 doxepin 80ecabapide 81 eletriptan 82 ellipticine 83 eplerenone 84 estazolam 85estradiol 86 estrone 87 etoperidone 88 etoricoxib 89 felodipine 90fenproporex 91 fentanyl 92 finasteride 93 flucloxacillin 94 fluoxetine95 fluvastatin 96 N-[2-(5-methoxy-1H-indol-3-yl)ethyl]-N-(propan-2-yl)propan-2-amine 97 gepirone 98 granisetron 992-[(R)-{[5-(cyclopropylmethoxy)pyridin-3-yl]methyl}sulfinyl]-5-fluoro-1H-benzimidazole 100 hydrocodone 101hydromorphone 102 ibogaine 103 ifosfamide 104 iloperidone 105 imipramine106 ketamine 107 ketobemidone 108N-carbamimidoyl-4-cyano-1-benzothiophene-2-carboxamide 109(1S,4S)-(6-dimethylamino-4,4-diphenyl-heptan-3-yl)acetate 110 laquinimod111 levobupivacaine 112 lidocaine 113 lisofylline 114 ropinirole 115loratadine 116 losartan 117 lovastatin 118[(4E)-7-chloro-4-[(sulfooxy)imino]-3,4-dihydroquinolin-1(2H)-yl](2-methylphenyl)methanone 119(2R)-1-(1,3-benzodioxol-5-yl)-N-ethylpropan-2-amine 120(2R)-1-(1,3-benzodioxol-5-yl)-N-methylpropan-2-amine 121 melatonin 122meloxicam 123 1-(4-methoxyphenyl)piperazine 124 methadone 125methoxychlor 126 methoxychlor-mono-oh 127 metoclopramide 128 mianserin129 midazolam 130 mirtazapine 1314-{[(5S)-2,4-dioxo-1,3-thiazolidin-5-yl]methyl}-2-methoxy-N-[4-(trifluoromethyl)benzyl]benzamide 132(2S)-1-(4-methylphenyl)-2-(pyrrolidin-1-yl)propan-1-one 1331-methyl-4-phenyl-1,2,3,6-tetrahydropyridine 134 mycophenolic-acid 135n-nitrosodiamylamine 136 naringenin 137 nefiracetam 138nn-dimethyl-m-toluamide 1394-[methyl(nitroso)amino]-1-(pyridin-3-yl)butan-1-one 140diethyl{4-[(4-bromo-2- cyanophenyl)carbamoyl]benzyl}phosphonate 141nordiazepam 142 nortriptyline 1435-(diethylamino)-2-methylpent-3-yn-2-yl(2S)-2-cyclohexyl-2-hydroxy-2-phenylacetate 144 ochratoxin-a 145 olanzapine 146olopatadine 147 (1R,2R,10R,11S,14R,15R)-14-ethynyl-14-hydroxy-15-methyl-17-methylidenetetracyclo[8.7.0.0{circumflex over ( )}{2,7}.0{circumflexover ( )}{11,15}]hepta deca-6,12-dien-5-one 148(3S)-3-[3-(methylsulfonyl)phenyl]-1-propylpiperidine 149 oxybutynin 150oxycodone 151 perazine 152 perphenazine 153 phenacetin 154 phencyclidine155 phenprocoumon 156 pimobendan 157 pradefovir 158 progesterone 159propafenone 160 pyrazoloacridine 161 quinacrine 162 rebamipide 163reboxetine 164 ropinirole 165 ropivacaine 166 roquinimex 167 safrole 168safrole 169 salmeterol 170 senecionine 171 seratrodast 172 seratrodast173 seratrodast 1747-chloro-N-({5-[(dimethylamino)methyl]cyclopenta-1,4-dien-1-yl}methyl)quinolin-4-amine 175 tamarixetin 176 tamsulosin 177tangeretin 178 tauromustine 179 temazepam 180 terbinafine 181terbuthylazine 182 testosterone 183 theophylline 184 tramadol 185trans-retinoic-acid 186 trazodone 187 triazolam 188 trofosfamide 189tropisetron 190 valdecoxib 191 192 193 yohimbine 194 zaleplon 195zolpidem 196 zotepine

Example 5 Comparison of Existing Metabolic Prediction Model withPrediction Model of the Present Invention

The present invention is an improved model compared to an existingmetabolic prediction model.

The existing QSAR model (Sheridan R P, Korzekwa K R, Torres R A, WalkerM J. J. Med. Chem. (2007) 50; 3173) and the present invention select twohighly possible positions, and the MetaSite program (Cruciani G,Carosati E, Boeck B D, Ethirajulu K, Mackie C, Howe T, Vianello R. J.Med. Chem. (2005)48; 6970) selects three highly possible positions.Thus, these cannot be directly compared with each other, but as can beseen in Table 3 below, the present invention shows improvedpredictability.

TABLE 10 Comparison of existing metabolic prediction model and inventiveprediction model 3A4 2D6 2C9 1A2 QSAR model^(a) 84% 70% 67% —MetaSite^(b) 72% 86% 86% 75% Invention^(a) 74% 70% 77% 78% ^(a)selectionof two highly possible positions ^(b)selection of three highly possiblepositions

Example 6 Prediction of Activation Energy Using Atomic Descriptors

6-1. Prediction of Activation Energy for Hydrogen Abstraction UsingAtomic Descriptors

Hydrogen abstraction by a cytochrome P450 enzyme may be shown in thefollowing reaction scheme 1:

wherein the cycle together with Fe—O indicates an oxyferrylintermediate.

In the present invention, the activation energy for cytochromeP450-mediated hydrogen abstraction from a substrate of the followingformula 1 was predicted using the atomic descriptors [δ_(het)],[max(δ_(heavy))], [μ_(C—H)] and

$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack:$

wherein the circle together with Fe—O indicates an oxyferrylintermediate;

$\begin{matrix}{{E_{a}^{{Habs\_}{(B)}} = {25.94 + {1.88*\lbrack \delta_{het} \rbrack} + {1.03*\lbrack {\max( \delta_{heavy} )} \rbrack}}};} & \lbrack {{Equation}\mspace{14mu} 1\text{-}1} \rbrack \\{E_{a}^{{Habs\_}{(A)}} = {28.50 - {2.22*\lbrack \mu_{C - H} \rbrack} + {1.12*\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack}}} & \lbrack {{Equation}\mspace{14mu} 1\text{-}2} \rbrack\end{matrix}$wherein E_(a) ^(Habs) ^(—) ^((B)) indicates activation energy requiredfor hydrogen attached to a carbon atom having a heteroatom (an atomother than carbon) in the alpha-position relative to the reactioncenter; E_(a) ^(Habs) ^(—) ^((A)) indicates activation energy requiredfor hydrogen attached to a carbon atom having no heteroatom (an atomother than carbon) in the alpha-position relative to the reactioncenter; and [δ_(het)] indicates the net atomic charge of a heteroatom(an atom other than carbon) in the alpha-position relative to thereaction center; [max(δ_(heavy))] indicates the highest atomic charge inX¹, X² and X³ which are neither hydrogen nor helium; [μ_(C—H)] indicatesthe bond dipole of the carbon-hydrogen bond; and

$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack$indicates the sum of the atomic polarizabilities of the atoms H, C, X¹,X² and X³.

6-2. Prediction of Activation Energy for Tetrahedral IntermediateFormation in Aromatic Hydroxylation Using Atomic Descriptors

Tetrahedral intermediate formation reaction in cytochrome P450-mediatedaromatic hydroxylation may be shown in the following reaction scheme 2:

wherein the circle together with O—Fe indicates an oxyferrylintermediate.

In the present invention, the activation energy for tetrahedralintermediate formation in cytochrome P450-mediated aromatichydroxylation of a substrate of the following formula 2 was predictedusing the atomic descriptors [δ_(H)] and [mean(α_(alpha))]:

wherein the circle together with Fe—O indicates an oxyferrylintermediate;E _(a) ^(aro) ^(—)^(o,p)=21.34−0.75*[δ_(H)]−1.24*[mean(α_(alpha)])  [Equation 2-1]E _(a) ^(aro) ^(m) =22.14−0.68*[δ_(H)]−0.83*[mean(α_(alpha))]  [Equation2-2]E _(a) ^(aro) ^(—)^(0,2,3)=21.02−1.49*[δ_(H)]−0.92*[mean(α_(alpha))]  [Equation 2-3]wherein E_(a) ^(aro) ^(—) ^(o,p) indicates the activation energy fortetrahedral intermediate formation in a benzene having one substituentin the ortho/para-position; E_(a) ^(aro) ^(—) ^(m) indicates theactivation energy for tetrahedral intermediate formation in a benzenehaving one substituent in the meta-position; E_(a) ^(aro) ^(—) ^(0,2,3)indicates the activation energy for tetrahedral intermediate formationin a benzene having 0, 2 or 3 substituents; [δ_(H)] indicates the netatomic charge of the hydrogen; and [mean(α_(alpha))] indicates the meanvalue of the polarizabilities of adjacent carbon atoms.

Example 7 Development of Model for Predicting Activation Energy forHydrogen Abstraction

The activation energy for hydrogen abstraction is a good measure forpredicting the regioselectivity of aliphatic hydroxylation anddehydroxylation in phase I metabolism.

wherein the circle together with Fe—O indicates an oxyferrylintermediate.

In order to model the above reaction, the activation energies of 431cases of 119 molecules were calculated using the AM1 (Austin Model 1)molecular orbital method.

Herein, the term “cases” refers to the number of atoms. For example, ifthere are 3 molecules having 3, 4 and 7 atoms, respectively, there willbe 14 cases of 3 molecules. The AM1 method is a semi-empirical methodfor quantum calculation of the electronic structures of molecules incomputational chemistry and is a generalization of the modified neglectof differential diatomic overlap approximation (Dewar, M. J. S. et al.,Journal of the American Chemical Society, 1985, 107, 3902).

The list of organic molecules calculated is shown in Table 11 below.

TABLE 11 Organic molecules used in training and verification forhydrogen abstraction (119 organic molecules) List of organic molecules(3-amino-propyl)-dimethyl-amine 1-chloro-4-methyl-pentane(3-bromo-propyl)-dimethyl-amine 1-chloro-butane(3-chloro-propyl)-dimethyl-amine 1-chloro-heptane(3-fluoro-propyl)-dimethyl-amine 1-chloro-hexane(3-iodo-propyl)-dimethyl-amine 1-chloromethyl-3-methyl-benzene1,2,3-trimethylbenzene 1-chloromethyl-4-methyl-benzene1,2,4-trimethylbenzene 1-chloro-octane 1,2-difluoro-3-methyl-butane1-chloro-pentane 1-bromo-2-methyl-benzene 1-chloro-propane1-bromo-3-methyl-benzene 1-ethoxy-3-fluoro-benzene1-bromo-4-methyl-benzene 1-ethyl-4-methylbenzene1-bromo-4-methyl-pentane 1-fluoro-2,4-dimethyl-pentane 1-bromo-heptane1-fluoro-2-methyl-benzene 1-bromo-hexane 1-fluoro-2-methyl-octane1-bromo-octane 1-fluoro-3-methyl-benzene 1-bromo-pentane1-fluoro-4-methyl-butane 1-bromo-propane 1-fluoro-4-methyl-benzene1-chloro-2-methylbenzene 1-fluoro-4-methyl-heptane1-chloro-3-methylbenzene 1-fluoro-4-methyl-pentane1-chloro-4-methylbenzene 1-fluoro-butane 1,2,3-trimethylbenzeneFluoro-benzene 1,2,4-trimethylbenzene Iodo-benzene1-ethyl-4-methylbenzene mesitylene 1-methyl-2-propylbenzenemethoxybenzene 1-o-tolylpropan-1-one m-xylene2,4-difluoro-1-methylbenzene n,4-dimethylbenzenamine2-fluoro-phenylamine o-xylene 2-methylanisol phenol3-fluoro-4-methylbenzeneamine propylbenzene 3-fluoro-phenylaminep-toluidine 4-ethoxy-aniline p-xylene 4-ethoxy-phenol4-fluoro-phenylamine aniline benzene benzenethiol chloro-benzenecyanobenzene ethoxybenzene ethylbenzene

Such information was used to train and evaluate the empirical equations.These cases include methyl, primary, secondary and tertiary carbonatoms, etc., in various chemical environments.

The present inventors divided these cases into two types depending onwhether electrically negative atoms (i.e. heteroatoms) exist around thebreaking carbon-hydrogen bond.

Equations modeled with atomic descriptors through the correlationbetween effective atomic descriptors and quantum-mechanically calculatedE_(a) for hydrogen abstraction are shown in Tables 12 and 13 below.

TABLE 12 Correlation between effective atomic descriptors and quantum-mechanically calculated E_(a) for hydrogen abstraction (the case ofhaving no heteroatom in the alpha-position) Training set Atomicdescriptor R^(a) RMSE^(b) Equation μ_(C-H) 0.88 0.63 E_(a) ^(Habs)_(A) =28.50-1.19*[μ_(C-H)] $\sum\limits_{i}^{R.C.}\alpha_{i}$ 0.67 1.00$E_{a}^{{Habs}\;\_\;{(A)}} = {28.50 - {0.90^{*}\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack}}$R^(a): correlation coefficient; RMSE^(b): root mean squared error.

TABLE 13 Correlation between effective atomic descriptors and quantum-mechanically calculated E_(a) for hydrogen abstraction (the case ofhaving a heteroatom in the alpha-position) Training set Atomicdescriptor R^(a) RMSE^(b) Equation δ_(het) 0.82 1.51 E_(a) ^(Habs) ^(—)^((B)) = 25.94 + 2.14 * [δ_(het)] max(δ_(heavy)) 0.57 2.16 E_(a) ^(Habs)^(—) ^((B)) = 25.94 + 1.51 * [max(δ_(heavy))] R^(a): correlationcoefficient; RMSE^(b): root mean squared error.

The present inventors performed the training processes shown in Tables12 and 13 above, thereby allowing linear equations to predict activationenergy in various chemical environments using two normalized effectiveatomic descriptors suited to each case (equations 1-1 and 1-2 below).

Among these effective atomic descriptors, [δ_(het)], [max(δ_(heavy))]and [μ_(C—H)] indicate the degree of weakness of the carbon-hydrogenbond, and

$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack$indicates the stability of transition states. In the present invention,all transition states were verified through the analysis of frequencies.

FIG. 3 is a flowchart showing a method of predicting activation energyusing the model of the present invention.

Specifically, the model for predicting the activation energy forCYP450-mediated hydrogen abstraction, developed in the presentinvention, comprises the following steps:

i) examining the metabolic position of a target molecule;

ii) determining the reaction type of the target molecule;

iii) determining the atomic type depending on whether there is aheteroatom in the alpha-position relative to the reaction center ofhydrogen abstraction;

iv) if there is a heteroatom in the alpha-position, calculating theatomic descriptors [δ_(het)] and [max(δ_(heavy))], and if there is noheteroatom in the alpha-position, calculating the atomic descriptors[μ_(C—H)] and

$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack;$

v) normalizing the atomic descriptors; and

vi) predicting activation energy according to the following equations:

wherein the circle together with O—Fe indicates an oxyferrylintermediate;

$\begin{matrix}{{E_{a}^{{Habs\_}{(B)}} = {25.94 + {1.88*\lbrack \delta_{net} \rbrack} + {1.03*\lbrack {\max( \delta_{heavy} )} \rbrack}}}{R = 0.91},{{RMSE} = 1.14},{n = 62},{{{P\mspace{14mu}{value}} < 0.0001};}} & \lbrack {{Equation}\mspace{14mu} 1\text{-}1} \rbrack \\{{E_{a}^{{Habs\_}{(A)}} = {28.50 - {2.22*\lbrack \mu_{C - H} \rbrack} + {1.12*\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack}}}{R = 0.95},{{RMSE} = 0.43},{n = 224},{{P\mspace{14mu}{value}} < 0.0001}} & \lbrack {{Equation}\mspace{14mu} 1\text{-}2} \rbrack\end{matrix}$wherein E_(a) ^(Habs) ^(—) ^((B)) indicates the activation energyrequired for abstraction of hydrogen attached to a carbon atom having aheteroatom (an atom other than carbon) in the alpha-position relative tothe reaction center; E_(a) ^(Habs) ^(—) ^((A)) indicates activationenergy required for abstraction of hydrogen attached to a carbon atomhaving no heteroatom (an atom other than carbon) in the alpha-positionrelative to the reaction center; [δ_(het)] indicates the net atomiccharge of a heteroatom (an atom other than carbon) in the alpha-positionrelative to the reaction center; [max(δ_(heavy))] indicates the highestatomic charge in X¹, X² and X³ which are neither hydrogen nor helium;[μ_(C—H)] indicates the bond dipole of the carbon-hydrogen bond; and

$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack$indicates the sum of the atomic polarizabilities of the atoms H, C, X¹,X² and X³.

In equations 1-1 and 1-2 above, R: correlation coefficient; RMSE: rootmean squared error; n: the number of atoms used in training; and Pvalue: the significance of the correlation coefficient.

In step i), any C—H bond to the target molecule can be regarded as aposition where metabolism can occur in the target molecule.

In step ii), if the carbon in any C—H bond to the target molecule isaliphatic carbon, it can be regarded as a position where H abstractionfrom the target molecule can occur.

In step iii), if there is a heteroatom in the alpha-position relative tothe reaction center (C—H where actual metabolism occurs), equation 1-1is used, and if there is no heteroatom in the alpha-position, equation1-2 is used.

In step v), the term “normalization” refers to normalizing the mean ofthe values of atomic descriptors to zero (0) and the standard deviationto 1, from a statistical viewpoint. Namely, before prediction,normalization is carried out using the mean and, standard deviation ofthe values of the atomic descriptors used in the training of theprediction model of the present invention.

As shown in FIG. 4, the activation energy predicted using the model ofthe present invention showed a high correlation with thequantum-mechanically calculated activation energy. 386 cases of 430cases are within chemical accuracy (1 kcal per mol). Some inconsistentcases are attributable to interactions other than carbon-hydrogen-oxygeninteractions during quantum mechanical calculation. Activation energiesof various molecules in a gaseous state were calculated using Gaussian03 [revision C.02, M. J. Frisch et al., Pittsburgh, Pa., USA, 2003].

Example 8 Verification of Activation Energy Predicted by Model forPredicting Activation Energy for Hydrogen Abstraction

Activation energies for hydrogen abstraction from the following fourmolecules, predicted using the prediction model of Example 7, wereverified by comparison with experimental values:

TABLE 14 Metabolic rate induced Predicted from activation activationExperimental Molecule #^([a]) energy^([b]) energy^([c]) metabolicrate^([d]) Hexane 1 26.89 4.1 4.5 2 28.20 46.6 49 3 28.16 49.3 46.5Octane 1 29.69 8.2 2.5 2 28.21 91.8 97.5 Ethylbenzene 1 30.32 0.1 0.2 225.73 99.9 99.8 1-chloromathyl-4- 1 27.51 12.5 16.0 methyl-benzene 226.31 87.5 84.0 ^([a])# indicates the atomic number of each molecule informula 2; ^([b])activation energy predicted by the method of thepresent invention; ^([c])metabolic rate induced by introducing thepredicted activation energy [b] into the Arrhenius equation; and^([d])in vitro experimental metabolic rate.

The experimental metabolic rates of the molecules shown in Table 14above are already known in the art. Specifically, the experimentalmetabolic rate of hexane can be found in the literature [Ken-ichirouMOROHASHI, Hiroyuki SADANO, Yoshiie OKADA, Tsuneo OMURA. PositionSpecificity in n-Hexane Hydroxylation by two forms of Cytochrome P450 inRat liver Microsomes. J. Biochem. 1983, 93, 413-419]; the experimentalmetabolic rate of octane in the literature [Jeffrey P. Jones, Allan E.Rettie, William F. Trager. Intrinsic Isotope Effects Suggest That theReaction Coordinate Symmetry for the Cytochrome P-450 CatalyzedHydroxylation of Octane Is Isozyme Independent. J. Med. Chem. 1990, 33,1242-1246]; the experimental metabolic rate of ethylbenzene can be foundin the literature [Ronald E. White, John P. Miller, Leonard V. Favreau,Apares Bhattacharyya. Stereochemical Dynamics of Aliphatic Hydroxylationby Cytochrome P-450. J. AM. Chem. Soc. 1986, 108, 6024-6031]; and theexperimental metabolic rate of 1-chloromethyl-4-methyl-benzene can befound in the literature [LeeAnn Higgins, Kenneth R. Korzekwa, StreedharaRao, Magong Shou, and Jeffrey P. Jones. An Assessment of the ReactionEnergetics for Cytochrome P450-Mediated Reactions. Arch. Biochem.Biophys. 2001, 385, 220-230].

As can be seen in Table 14 above, when the metabolic rates^([c])(induced by substituting into the Arrhenius equation the activationenergies for hydrogen abstraction from the four molecules, hexane,octane, ethylbenzene and 1-chloromethyl-4-methyl-benzene, predictedaccording to the present invention) were compared with the experimentalmetabolic rates^([d]), these metabolic rates showed similar tendencies.This suggests that the experimental metabolic rates can be predictedthrough the activation energies predicted according to the presentinvention.

Example 9 Development of Model for Predicting the Activation Energy forTetrahedral Intermediate Formation in Aromatic Hydroxylation

The present inventors modeled tetrahedral intermediate formation servingas a good measure of the regioselectivity of aromatic hydroxylation inphase I metabolism.

wherein the circle together with Fe—O indicates an oxyferrylintermediate.

To model the above reaction, the activation energies of 85 cases of 31benzene molecules in various chemical environments were calculated usingthe AM1 (Austin Model 1) molecular orbital method.

Herein, the term “cases” refers to the number of atoms. For example, ifthere are 3 molecules having 3, 4 and 7 atoms, respectively, there willbe 14 cases of 3 molecules. The AM1 method is a semi-empirical methodfor quantum calculation of the electronic structures of molecules incomputational chemistry and is a generalization of the modified neglectof differential diatomic overlap approximation (Dewar, M. J. S. et al.,Journal of the American Chemical Society, 1985, 107, 3902).

The list of organic molecules calculated is shown in Table 15 below.

TABLE 15 Organic molecules used in training and verification fortetrahedral intermediate formation (31 organic molecules) List oforganic molecules 1,2,3-trimethylbenzene Fluoro-benzene1,2,4-trimethylbenzene Iodo-benzene 1-ethyl-4-methylbenzene mesitylene1-methyl-2-propylbenzene methoxybenzene 1-o-tolylpropan-1-one m-xylene2,4-difluoro-1-methylbenzene n,4-dimethylbenzenamine2-fluoro-phenylamine o-xylene 2-methylanisol phenol3-fluoro-4-methylbenzenamine propylbenzene 3-fluoro-phenylaminep-toluidine 4-ethoxyaniline p-xylene 4-ethoxy-phenol4-fluoro-phenylamine aniline benzene benzenethiol chloro-benzenecyanobenzene ethoxybenzene ethylbenzene

Such information was used to train and evaluate the empirical equations.These cases were divided into three types: i) having one substituent inthe ortho/para position; ii) having one substituent in themeta-position; and iii) having 0, 2 or 3 substituents.

Equations modeled with atomic descriptors through the correlationbetween effective atomic descriptors and quantum-mechanically calculatedE_(a) for aromatic hydroxylation are shown in Tables 16, 17 and 18below.

TABLE 16 Correlation between effective atomic descriptors and quantum-mechanically calculated E_(a) for aromatic hydroxylation (the case ofhaving a substituent in the ortho-position) Training set Atomicdescriptor R^(a) RMSE^(b) Equation δ_(H) 0.08 1.31 E_(a) ^(aro) _(—)^(o,p) = 14.67 + 63.33 * [δ_(H)] α_(alpha) 0.57 1.07 E_(a) ^(aro) _(—)^(o,p) = 61.60 − 26.53 * [α_(alpha)] R^(a): correlation coefficient;RMSE^(b): root mean squared error.

TABLE 17 Correlation between effective atomic descriptors and quantum-mechanically calculated E_(a) for aromatic hydroxylation (the case ofhaving a substituent in the meta-position) Training set Atomicdescriptor R^(a) RMSE^(b) Equation δ_(H) 0.03 0.56 E_(a) ^(aro) _(—)^(m) = −12.61 + 333.87 * [δ_(H)] α_(alpha) 0.50 0.49 E_(a) ^(aro) _(—)^(m) = 132.75 − 72.54 * [α_(alpha)] R^(a): correlation coefficient;RMSE^(b): root mean squared error.

TABLE 18 Correlation between effective atomic descriptors and quantum-mechanically calculated E_(a) for aromatic hydroxylation (the case ofhaving 0, 2 or 3 substituents) Training set Atomic descriptor R^(a)RMSE^(b) Equation δ_(H) 0.69 0.95 E_(a) ^(aro) _(—) ^(0.2,3) = 70.00 −465.88 * [δ_(H)] α_(alpha) 0.05 1.31 E_(a) ^(aro) _(—) ^(0.2,3) =17.65 + 2.21 * [α_(alpha)] R^(a): correlation coefficient; RMSE^(b):root mean squared error.

The present inventors performed the training processes shown in Tables16 to 18 above, thereby allowing linear equations to predict activationenergy in various chemical environments using two normalized effectiveatomic descriptors suited to each case (equations 2-1, 2-2 and 2-3below).

Among effective atomic descriptors which are used in the equations forpredicting the activation energy for tetrahedral intermediate formation,[δ_(H)] determines the proximity between oxygenating species andsubstrate, and [mean (α_(alpha))] is related to the stability oftransition states. In the present invention, para-nitrosophenoxy radical(PNR) was used as oxygenating species, and all transition states wereverified through the analysis of frequencies.

FIG. 3 shows a flowchart showing a method of predicting activationenergy using the model used in the present invention.

Specifically, the model for predicting the activation energy fortetrahedral intermediate formation in CYP450-mediated aromatichydroxylation, developed in the present invention, comprises thefollowing steps:

i) examining the metabolic position of a target molecule;

ii) determining the reaction type of the target molecule;

iii) if the reaction type in step ii) is determined to be aromatichydroxylation, calculating the atomic descriptors [δ_(H)] and[mean(α_(alpha))];

iv) normalizing the atomic descriptors; and

v) predicting activation energy according to the following equations:

wherein the circle together with Fe—O indicates an oxyferrylintermediate;E _(a) ^(aro) ^(—) ^(o,p)=21.34−0.75*[δ_(H)]−1.24*[mean(α_(alpha)])R=0.71, RMSE=0.95, n=16, P value=0.009;  [Equation 2-1]E _(a) ^(aro) ^(m) =22.14−0.68*[δ_(H)]−0.83*[mean(α_(alpha))]R=0.88, RMSE=0.30, n=8, P value=0.026;  [Equation 2-2]E _(a) ^(aro) ^(—) ^(0,2,3)=21.02−1.49*[δ_(H)]−0.92*[mean(α_(alpha))]R=0.87, RMSE=0.65, n=33, P

<0.0001  [Equation 2-3]wherein E_(a) ^(aro) ^(—) ^(o,p) indicates the activation energy fortetrahedral intermediate formation in a benzene having one substituentin the ortho/para position; E_(a) ^(aro) ^(—) ^(m) indicates theactivation energy for tetrahedral intermediate formation in a benzenehaving one substituent in the meta-position; E_(a) ^(aro) ^(—) ^(0,2,3)indicates the activation energy for tetrahedral intermediate formationin a benzene having 0, 2 or 3 substituents; [δ_(H)] indicates the netatomic charge of the hydrogen; and [mean(α_(alpha))] indicates the meanof the polarizabilities of adjacent carbon atoms.

In equations 2-1, 2-2 and 2-3 above, R: correlation coefficient; RMSE:root mean squared error; n: the number of atoms used in training; and Pvalue: the significance of the correlation coefficient.

As shown in FIG. 5, the activation energy predicted using the model ofthe present invention showed a high correlation with thequantum-mechanically calculated activation energy. 70 cases of 85 casesare within chemical accuracy (1 kcal per mol). Some inconsistent casesoccurred because the model did not consider the ortho-, meta- andpara-effects when modeling the benzene molecule having 0, 2 or 3substituents. Activation energies of various molecules in a gaseousstate were calculated using Gaussian 03 [revision C.02, M. J. Frisch etal., Pittsburgh, Pa., USA, 2003].

Example 10 Verification of Activation Energy Predicted by Model forPredicting Activation Energy for Tetrahedral Intermediate Formation inAromatic Hydroxylation

The activation energies for tetrahedral intermediate formation for thefollowing two molecules, predicted by the prediction model of Example 9,were verified by comparison with experimental values.

TABLE 19 Metabolic rate induced Predicted from activation activationExperimental Molecule #^([a]) energy^([b]) energy^([c]) metabolicrate^([d]) Methoxybenzene 2 21.79 30.8 15-24 3 22.41 11.1 1-3 4 21.4058.1 62-75 Chlorobenzene 2 22.81 8.0 17-19 3 22.58 11.6 5-9 4 21.39 80.471-79 ^([a])# indicates the atomic number of each molecule in formula 4;^([b])activation energy predicted by the method of the presentinvention; ^([c])metabolic rate induced by introducing the predictedactivation energy [b] into the Arrhenius equation; and ^([d])in vitroexperimental metabolic rate.

The experimental metabolic rates of the molecules shown in Table 19above are already known in the art. Specifically, the experimentalmetabolic rate of methoxybenzene can be found in the literature [RobertP. Hanzlik, Kerstin Hogberg, Charles M. Judson. Microsomal hydroxylationof specifically deuterated monosubstituted benzenes. Evidence for directaromatic hydroxylation. Biochemistry. 1984, 23, 3048-3055]; and thechlorobenzene can be found in the literature [H. G. Selander, D. M.Jerina, J. W. Daly. Metabolism of Chlorobenzene with Hepatic Microsomesand Solubilized Cytochrome P-450 Systems. Arch. Biochem. Biophys. 1975,168, 309-321].

As can be seen in Table 19 above, when the metabolic rates (induced bysubstituting into the Arrhenius equation the activation energies forhydrogen abstraction from the two molecules, methoxybenzene andchorobenzene, predicted according to the present invention) werecompared with the experimental metabolic rates^([d]), these metabolicrates showed similar tendencies. This suggests that the experimentalmetabolic rates can be predicted through the activation energiespredicted according to the present invention.

As described above, the method of the present invention can rapidlypredict activation energy for phase I metabolites at a practical levelwithout having to perform a docking experiment between any additionalCYP450 and the substrate, or a quantum mechanical calculation, therebymaking it easier to develop new drugs using a computer. Also, thepresent invention may propose a strategy for increasing thebioavailability of drugs through the avoidance of metabolites based onthe possibility of drug metabolism. Furthermore, the method of thepresent invention proposes new empirical approaches which can also beeasily applied to activation energies for various chemical reactions,and makes it possible to explain physical and chemical factors thatdetermine activation energy. In addition, through the prediction ofactivation energy according to the present invention, it is possible topredict i) metabolic products, ii) the relative rate of metabolism, iii)metabolic regioselectivity, iv) metabolic inhibition, v) drug-druginteractions, and vi) the toxicity of a metabolite.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrative,and not restrictive. The scope of the invention is, therefore, indicatedby the appended claims, rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within the scope of the present invention.

What is claimed is:
 1. A method for constructing a database of atomicfingerprint descriptors by a processor for predicting a reaction ratefor a cytochrome P450-mediated reaction on a substrate, the methodcomprising the steps of: (i) providing an atomic fingerprint descriptorof a substrate, which is obtained by characterizing one or more atomicpositions of the substrate according to the following Equation 1:Xabc  [Equation 1] wherein X is the chemical symbol of an atom; a is abond indicator noting the number of atoms bonded; b is a ring indicatornoting whether the atom is part of a ring; and c is an aromaticindicator noting whether the atom is an aromatic atom; (ii) calculatingan activation energy for the one or more atomic positions of step (i)based on two or more atomic descriptors which valuate atomicinteractions at said atomic position, wherein the two or more atomicdescriptors are selected from the group consisting of [δ_(het)],[max(δ_(heavy))], [μ_(C—H)],$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack,$[δ_(H)], and [mean (α_(alpha))]; (iii) predicting a reaction rate for acytochrome P450-mediated reaction by introducing the calculatedactivation energy obtained in step (ii) into an Arrhenius equation; (iv)performing a statistical correlation which relates the predictedreaction rate of step (iii) with an experimental reaction rate; and (v)storing the predicted reaction rate, the experimental reaction rate, andthe statistical correlation for each atomic fingerprint descriptor ofthe substrate, into a database of atomic fingerprint descriptors forpredicting reaction rates for cytochrome P450-mediated reactions of asubstrate.
 2. The method of claim 1, wherein the cytochromeP450-mediated reaction on a substrate is aliphatic hydroxylation oraromatic hydroxylation.
 3. The method of claim 1, wherein the cytochromeP450-mediated reaction on a substrate is N-dealkylation,C-hydroxylation, N-oxidation, or O-dealkylation.
 4. The method of claim1, wherein an enzyme catalyzing the cytochrome P450-mediated reaction isselected from the group consisting of CYP2E1, CYP3A4, CYP2B6, CYP2C8,CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1, and CYP2A6.
 5. Themethod of claim 1, wherein the cytochrome P450-mediated reaction is acytochrome P450-mediated hydrogen abstraction on a substrate of thefollowing Formula 1:

wherein the circle together with Fe—O indicates an oxyferrylintermediate; wherein the two or more atomic descriptors are [δ_(het)],[max(δ_(heavy))], [μ_(C—H)], and$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack;$ andwherein [δ_(het)] indicates the net atomic charge of a heteroatom in thealpha-position relative to the reaction center, [max(δ_(heavy))],indicates the highest atomic charge in X¹, X² and X³ which are neitherhydrogen nor helium, [μ_(C—H)] indicates the bond dipole of thecarbon-hydrogen bond, and$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack$indicates the sum of the atomic polarizabilities of H, C, X¹, X² and X³.6. The method of claim 5, wherein the activation energy is calculated instep (ii) according to the following equation:E _(a) ^(Habs) ^(—) ^((B))=25.94+1.88*[δ_(het)]+1.03*[max(δ_(heavy))]wherein E_(a) ^(Habs) ^(—) ^((B)) indicates activation energy requiredfor abstraction of hydrogen attached to a carbon atom having aheteroatom in the alpha-position relative to the reaction center.
 7. Themethod of claim 5, wherein the activation energy is calculated in step(ii) according to the following equation:$E_{a}^{{Habs\_}{(A)}} = {28.50 - {2.22*\lbrack \mu_{C - H} \rbrack} + {1.12*\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack}}$wherein E_(a) ^(Habs) ^(—) ^((A)) indicates activation energy requiredfor abstraction of hydrogen attached to a carbon atom having noheteroatom in the alpha-position relative to the reaction center.
 8. Themethod of claim 1, wherein the cytochrome P450-mediated reaction isformation of a tetrahedral intermediate which occurs via a cytochromeP450-mediated aromatic hydroxylation on a substrate of the followingFormula 2:

wherein the circle together with Fe—O indicates an oxyferrylintermediate; wherein the two or more atomic descriptors are [δ_(H)] and[mean(α_(alpha))]; and wherein [δ_(H)] indicates the net atomic chargeof the hydrogen of the substrate and [mean(α_(alpha))] indicates themean value of the polarizabilities of adjacent carbon atoms.
 9. Themethod of claim 8, wherein the activation energy is calculated in step(ii) according to the following equation:E _(a) ^(aro) ^(—) ^(o,p)=21.34−0.75*[δ_(H)]−1.24*[mean(α_(alpha))]wherein E_(a) ^(aro) ^(—) ^(o,p) indicates the activation energy fortetrahedral intermediate formation in a benzene having one substituentin the ortho/para-position.
 10. The method of claim 8, wherein theactivation energy is calculated in step (ii) according to the followingequation:E _(a) ^(aro) ^(—) ^(m)=22.14−0.68*[δ_(H)]−0.83*[mean(α_(alpha))]wherein E_(a) ^(aro) ^(—) ^(m) indicates the activation energy fortetrahedral intermediate formation in a benzene having one substituentin the meta-position.
 11. The method of claim 8, wherein the activationenergy is calculated in step (ii) according to the following equation:E _(a) ^(aro) ^(—) ^(0,2,3)=21.02−1.49*[δ_(H)]−0.92*[mean(α_(alpha))]wherein E_(a) ^(aro) ^(—) ^(0,2,3) indicates the activation energy fortetrahedral intermediate formation in a benzene having 0, 2, or 3substituents.
 12. A method for a processor to calculate an activationenergy for a cytochrome P450-mediated hydrogen abstraction on asubstrate of the following Formula 1:

wherein the circle together with Fe—O indicates an oxyferrylintermediate; and wherein the atomic position comprises two or moreatomic descriptors selected from the group consisting of [δ_(het)],[max(δ_(heavy))], [μ_(C—H)], and$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack;$ andwherein [δ_(het)] indicates the net atomic charge of a heteroatom in thealpha-position relative to the reaction center, [max(δ_(heavy))]indicates the highest atomic charge in X¹, X² and X³ which are neitherhydrogen nor helium, [μ_(C—H)] indicates the bond dipole of thecarbon-hydrogen bond, and$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack$indicates the sum of the atomic polarizabilities of the atoms H, C, X¹,X² and X³; the method further comprise making a comparison of thecalculated activation energy to a database of atomic fingerprintdescriptors; wherein the comparison provides a prediction regarding oneor more of metabolic products, relative rate of metabolism, metabolicregioselectivity, metabolic inhibition, drug-drug interactions, andtoxicity of a metabolite.
 13. The method of claim 12, wherein thecytochrome P450-mediated hydrogen abstraction is catalyzed by acytochrome P450 enzyme selected from the group consisting of CYP2E1,CYP3A4, CYP2B6, CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1,and CYP2A6.
 14. The method of claim 12, wherein hydrogen abstraction canoccur if a C atom of a C—H bond in the substrate is an aliphatic carbon.15. The method of claim 12, wherein the two or more atomic descriptorsare [δ_(het)] and [max(δ_(heavy))] when there is a heteroatom in thealpha-position relative to the reaction center and wherein theactivation energy is calculated according to the following equation:E _(a) ^(Habs) ^(—) ^((B))=25.94+1.88*[δ_(het)]+1.03*[max(δ_(heavy))].16. The method of claim 12, wherein the two or more atomic descriptorsare [μ_(C—H)] and$\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack$ whenthere is no heteroatom in the alpha-position relative to the reactioncenter and wherein the activation energy is calculated according to thefollowing equation:$E_{a}^{{Habs\_}{(A)}} = {28.50 - {2.22*\lbrack \mu_{C - H} \rbrack} + {1.12*{\lbrack {\sum\limits_{i}^{R.C.}\alpha_{i}} \rbrack.}}}$17. A method for a processor to calculate an activation energy forformation of a tetrahedral intermediate by a cytochrome P450-mediatedaromatic hydroxylation at an atomic position in a substrate of thefollowing Formula 2:

wherein the circle together with Fe—O indicates an oxyferrylintermediate; wherein the atomic position comprises atomic descriptors[δ_(H)] and [mean(α_(alpha))]; and wherein [δ_(H)] indicates the netatomic charge of the hydrogen of the substrate and [mean(α_(alpha))]indicates the mean values of polarizabilities of adjacent carbon atoms;the method further comprises making a comparison of the calculatedactivation energy to a database of atomic fingerprint descriptors;wherein the comparison provides a prediction regarding one or more ofmetabolic products, relative rate of metabolism, metabolicregioselectivity, metabolic inhibition, drug-drug interactions, andtoxicity of a metabolite.
 18. The method of claim 17, wherein thecytochrome P450-mediated aromatic hydroxylation is catalyzed by acytochrome P450 enzyme selected from the group consisting of CYP2E1,CYP3A4, CYP2B6, CYP2C8, CYP2C9, CYP1A1, CYP1A2, CYP2C19, CYP2D6, CYP1B1,and CYP2A6.
 19. The method of claim 17, wherein aromatic hydroxylationcan occur if a C atom of a C—H bond in the substrate is an aliphaticcarbon.
 20. The method of claim 17, wherein the atomic descriptors[δ_(H)] and [mean(α_(alpha))] are determined.
 21. The method of claim20, wherein the activation energy is calculated according to thefollowing equation:E _(a) ^(aro) ^(—) ^(o,p)=21.34−0.75*[δ_(H)]−1.24*[mean(α_(alpha))]wherein E_(a) ^(aro) ^(—) ^(o,p) indicates the activation energy fortetrahedral intermediate formation in a benzene having one substituentin the ortho/para-position.
 22. The method of claim 20, wherein theactivation energy is calculated according to the following equation:E _(a) ^(aro) ^(—) ^(m)=22.14−0.68*[δ_(H)]−0.83*[mean(α_(alpha))]wherein E_(a) ^(aro) ^(—) _(m) indicates the activation energy fortetrahedral intermediate formation in a benzene having one substituentin the meta-position.
 23. The method of claim 20, wherein the activationenergy is calculated according to the following equation:E _(a) ^(aro) ^(—) ^(0,2,3)=21.02−1.49*[δ_(H)]−0.92*[mean(α_(alpha))]wherein E_(a) ^(aro) ^(—) ^(0,2,3) indicates the activation energy fortetrahedral intermediate formation in a benzene having 0, 2, or 3substituents.